Playing with my Raspberry Pi

Introduction

I do most of my work (like writing this blog posting) on my MacBook Air laptop. I used to have a good desktop computer for running various longer running processes or playing games. Last year the desktop packed it in (it was getting old anyway), so since then I’ve just been using my laptop. I wondered if I should get another desktop and run Ubuntu on it, since that is good for machine learning, but I wondered if it was worth price. Meanwhile I was intrigued with everything I see people doing with Raspberry Pi’s. So I figured why not just get a Raspberry Pi and see if I can do the same things with it as I did with my desktop. Plus I thought it would be fun to learn about the Pi and that it would be a good toy to play with.

Setup

Since I’m new to the Raspberry Pi, I figured the best way to get started was to order one of the starter kits. This way I’d be able to get up and running quicker and get everything I needed in one shot. I had a credit with Amazon, so I ordered one of the Canakits from there. It included the Raspberry Pi 3, a microSD card with Raspbian Linux, a case, a power supply, an electronics breadboard, some leds and resistors, heat sinks and an HDMI cable. Then I needed to supply a monitor, a USB keyboard and a USB mouse (which I had lying around).

Setting up was quite easy, though the quick setup instructions were missing a few steps like what to do with the heatsinks (which was obvious) or how to connect the breadboard. Setup was really just install the Raspberry Pi motherboard in the case, add the heat sinks, insert the microSD card and then connect the various cables.

As soon as I powered it on, it displayed an operating system selection and installation menu (with only one choice), so clicked install and 10 minutes later I was logged in and running Raspbian.

The quick setup guide then recommends you set your locale and change the default password, but they don’t tell you the existing password, which a quick Google reveals as “Raspberry”. Then I connected to our Wifi network and I was up and running. I could browse the Internet using Chromium, I could run Mathematica (a free Raspberry version comes pre-installed), run a Linux terminal session. All rather painless and fairly straight forward.

I was quite impressed how quickly it went and how powerful a computer I had up and running costing less than $100 (for everything) and how easy the installation and setup process was.

Software

I was extremely pleased with how much software the Raspberry Pi came with pre-installed. This was all on the provided 32Gig card, which with a few extra things installed, I still have 28Gig free. Amazingly compact. Some of the pre-installed software includes:

Mathematica. Great for Math students and to promote Mathematica. Runs from the Wolfram Language which is interesting in itself.

Plus there is an add/remove software program where you can easily add many more open source Pi programs. You can also use the Linux apt-get command to get many other pre-compiled packages.

Generally I would say this is a very complete set of software for any student, hobbyist or even office worker.

Python

I use Python as my main goto programming language these days and generally I use a number of scientific and machine learning libraries. So I tried installing these. Usually I just use pip3 and away things go (at least on my Mac). However doing this caused pip3 to download the C++/Fortran source code and to try to compile it, which failed. I then Googled around on how to best install these packages.

Unfortunately most of the Google results were how to do this for Python 2, which I didn’t want. It will be so nice when Python 2 finally is discontinued and stops confusing everything. I wanted these for Python 3. Before you start you should update apt-get’s list of available software and upgrade all the packages on your machine. You can do this with:

However I couldn’t find and apt-get module for SciKit Learn the machine learning library. So I tried pip3 and it did work even though it downloaded the source code and compiled it.

pip3 install sklearn –upgrade

Now I had all the scientific programming power of the standard Python libraries. Note that since the Raspberry Pi only has 1Gig RAM and the SD Card only has twenty something Gig free, you can’t really run large machine learning tasks. However if they do fit within the Pi then it is a very inexpensive way to do these computations. What a lot of people do is build clusters of Raspberry Pi’s that work together. I’ve seen articles on how University labs have built supercomputers out of hundreds or Pi’s all put together in a cluster. Further they run quite sophisticated software like Hadoop, Docker and Kubernetes to orchestrate the whole thing.

Summary

I now have the Raspberry Pi up and running and I’m enjoying playing with Mathematica and Sonic Pi. I’m doing a bit of Python programming and browsing the Internet. Quite an amazing little device. I’m also impressed with how much it can do for such a low cost. As other vendors like Apple, Microsoft, HP and Dell try to push people into more and more expensive desktops and laptops, it will be interesting to see how many people revolt and switch to the far more inexpensive DIY type solutions. Note that there are vendors that make things like Raspberry Pi complete desktop computers at quite a low cost as well.